Supply Chain Financial Plan Vs. Actuals analytics


The automated solution significantly improved the company’s approach to outbound logistics cost auditing and integrated cost planning.

Aggregate, extract, normalize financial plans from distributed planning systems. Reconcile with actuals. Analytics for performance management and future planning

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Business Problem

  • Lack of visibility into carrier logistics at a major US retailer hampered efforts to determine how to improve logistics financial planning, a billion dollar item, to align with the execution costs
  • And whether the costs were in line with pre-determined agreements.
  • There were three unknowns: a) if the shipping routes were being executed by the carriers per the execution plan; b) if the shipping routes billed by the carrier matched the ones in the execution system; and c) whether the invoices aligned to contracted rates.
  • All of this information was distributed across 35 distribution centers and numerous IT systems in a combination of unstructured and structured formats.

 

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Solution

  • RAGE-AI Intelligent Machine to extract greater insights from the financial planning data, transportation data.
  • Automated cost auditing applying machine learning to review tens of thousands of invoices and contracts to obtain origination of cost variances and the financial impact.
  • Automated matching of data from invoices and contracts with the execution system to discover the actual costs, which was then used for financial planning purposes.

 

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Benefits

  • Realized ~$40 million in cost leakage on a $600 million spend.
  • By identifying gaps between planning and actual performance, the automated solution significantly improved the company's approach to outbound logistics cost auditing and integrated cost planning.